An on-line retailing firm conducts a study into the number of on-line purchases of footwear made during all of last year. The study wants to determine the proportion of Australians over the age of 25 who purchase footwear on-line. The managing director of the on-line retailing firm believes this proportion of Australians is 22%. A random sample of 179 Australians over the age of 25 is selected. If the managing director’s claim is true, what is the sample proportion value below which 10% of all sample proportions would exist? Express your answer correct to two decimal places.
Question
An on-line retailing firm conducts a study into the number of on-line purchases of footwear made during all of last year. The study wants to determine the proportion of Australians over the age of 25 who purchase footwear on-line. The managing director of the on-line retailing firm believes this proportion of Australians is 22%. A random sample of 179 Australians over the age of 25 is selected. If the managing director’s claim is true, what is the sample proportion value below which 10% of all sample proportions would exist? Express your answer correct to two decimal places.
Solution
To find the sample proportion value below which 10% of all sample proportions would exist, we first need to find the standard error for the sampling distribution of the proportion. The standard error is calculated using the formula:
SE = sqrt[p(1 - p) / n]
where p is the proportion (0.22 in this case) and n is the sample size (179 in this case).
SE = sqrt[0.22(1 - 0.22) / 179] = 0.032 (rounded to three decimal places)
We then need to find the z-score that corresponds to the 10th percentile in a standard normal distribution. We can look up this value in a standard normal distribution table or use a calculator with a normal distribution function. The z-score that corresponds to the 10th percentile is approximately -1.28.
We then use this z-score to find the corresponding value in the distribution of sample proportions. We use the formula:
p̂ = p + ZSE
where p̂ is the sample proportion we are interested in, p is the proportion (0.22), Z is the z-score (-1.28), and SE is the standard error (0.032).
p̂ = 0.22 + (-1.28)(0.032) = 0.18 (rounded to two decimal places)
So, the sample proportion value below which 10% of all sample proportions would exist is 0.18.
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